# Computations
import numpy as np
import pandas as pd

# scipy
from scipy.stats import norm

# preprocessing
from sklearn import preprocessing
import re

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex

## seaborn
import seaborn as sns
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})
sns.set_style("white")

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors

plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
# Graphics in retina format 
%config InlineBackend.figure_format = 'retina' 

## Progress Bar
import progressbar

## missingno
import missingno as msno

import warnings
warnings.filterwarnings("ignore")

IBM HR Analytics Employee Attrition and Performance Dataset

In this study, we analyze HR data available from kaggle.com

This data is fictional and it is created by IBM data scientists.

Categorical Parameters:

	1	2	3	4	5
Education	Below College	College	Bachelor	Master	Doctor
Environment Satisfaction	Low	Medium	High	Very High
Job Involvement	Low	Medium	High	Very High
Job Satisfaction	Low	Medium	High	Very High
Performance Rating	Low	Good	Excellent	Outstanding
Relationship Satisfaction	Low	Medium	High	Very High
WorkLife Balance	Bad	Good	Better	Best

This can be encoded as follows,

Categorical_Dict = {'Education': {1:'Below College', 2:'College',3:'Bachelor', 4: 'Master', 5:'Doctor'},
                    'Environment Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Involvement': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Job Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Performance Rating': {1:'Low', 2:'Good', 3:'Excellent', 4:'Outstanding'},
                    'Relationship Satisfaction': {1:'Low', 2:'Medium', 3:'High', 4:'Very High'},
                    'Work Life Balance': {1:'Bad', 2:'Good', 3:'Better', 4:'Best'}}

Loading the Dataset

Data = pd.read_excel('Data/WA_Fn-UseC_-HR-Employee-Attrition.xlsx')
Temp = [re.sub(r"(\w)([A-Z])", r"\1 \2", x) for x in Data.columns]
Temp = [x.replace(' Curr ', ' Current ').replace('18',' 18').replace('Num ','Number Of ') for x in Temp]
Data.columns = Temp
del Temp
Labels = ['Remanined', 'Churned']
Data.head().style.hide_index()

Age	Attrition	Business Travel	Daily Rate	Department	Distance From Home	Education	Education Field	Employee Count	Employee Number	Environment Satisfaction	Gender	Hourly Rate	Job Involvement	Job Level	Job Role	Job Satisfaction	Marital Status	Monthly Income	Monthly Rate	Number Of Companies Worked	Over 18	Over Time	Percent Salary Hike	Performance Rating	Relationship Satisfaction	Standard Hours	Stock Option Level	Total Working Years	Training Times Last Year	Work Life Balance	Years At Company	Years In Current Role	Years Since Last Promotion	Years With Current Manager
41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

First off, let's take a look at the dataset

def Data_info(Inp, Only_NaN = False):
    Out = pd.DataFrame(Inp.dtypes,columns=['Data Type']).sort_values(by=['Data Type'])
    Out = Out.join(pd.DataFrame(Inp.isnull().sum(), columns=['Number of NaN Values']), how='outer')
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out
display(Data_info(Data).T[:2])
_ = msno.bar(Data, figsize=(16,3), fontsize=14, log=False, color="#34495e")

	Age	Attrition	Business Travel	Daily Rate	Department	Distance From Home	Education	Education Field	Employee Count	Employee Number	...	Relationship Satisfaction	Standard Hours	Stock Option Level	Total Working Years	Training Times Last Year	Work Life Balance	Years At Company	Years In Current Role	Years Since Last Promotion	Years With Current Manager
Data Type	int64	object	object	int64	object	int64	int64	object	int64	int64	...	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64
Number of NaN Values	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

2 rows × 35 columns

Furthermore, the datatype for each columns

def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Columns'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Columns'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
    return Out
dtypes = dtypes_group(Data)
display(dtypes)

	Columns
int64	[Age, Years In Current Role, Years At Company,...
object	[Over 18, Job Role, Education Field, Gender, D...

_ = Data.hist(bins=30, grid=False, figsize=(18,18), color='#34495e', edgecolor='k', zorder=2, rwidth=0.8)

Exploratory Data Analysis

## Attrition Colormap
Att_Colors = ['LightSalmon', 'LightBlue']
Att_LC = 'Black'
# Gender Colormap
MF_Colors = ['HotPink', 'RoyalBlue']
MF_LC = 'Navy'
# Education
Ed_Colors = ['LightCoral','Khaki','GreenYellow','LimeGreen','ForestGreen']
Ed_LC = 'Black'

Age

fig = px.histogram(Data, x = 'Age', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 80])
fig.update_layout(title={'text': 'Age Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

bins = pd.IntervalIndex.from_tuples([(0, 25), (25, 40), (40, 45),(45, 60)])
Temp = Data[['Gender','Age','Attrition']]
Temp['Age'] = pd.cut(Temp['Age'], bins)
Temp = Temp.groupby(['Gender','Age','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Age'])
Temp['Age'] = Temp['Age'].astype(str)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Age', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Age', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 25], row=2, col=1)
fig.update_yaxes(title_text='Age', row=1, col=1)
fig.update_yaxes(title_text='Age', row=2, col=1)
fig.update_layout(title={'text': 'Age Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

bins = pd.IntervalIndex.from_tuples([(0, 25), (25, 40), (40, 45),(45, 60)])
Temp = Data[['Education','Age','Attrition']]
Temp['Age'] = pd.cut(Temp['Age'], bins)
Temp = Temp.groupby(['Education','Age','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Age'], inplace = True)
Temp['Age'] = Temp['Age'].astype(str)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(13.5, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Age', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,20])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Age', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Business Travel

Temp = Data.groupby(['Gender','Business Travel','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Business Travel'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Business Travel', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Business Travel', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 500)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 40], row=2, col=1)
fig.update_yaxes(title_text='Business Travel', row=1, col=1)
fig.update_yaxes(title_text='Business Travel', row=2, col=1)
fig.update_layout(title={'text': 'Business Travel Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Department

Temp = Data.groupby(['Gender','Department','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Department'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Department', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Department', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 500)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 35], row=2, col=1)
fig.update_yaxes(title_text='Department', row=1, col=1)
fig.update_yaxes(title_text='Department', row=2, col=1)
fig.update_layout(title={'text': 'Department Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Temp = Data[['Education','Department','Attrition']]
Temp = Temp.groupby(['Education','Department','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Department'], inplace = True)
Temp['Department'] = Temp['Department'].map(lambda x: x.replace('&','and').replace(' ','\n'))
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(13.5, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Department', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,25])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Department', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Distance from Home

fig = px.histogram(Data, x = 'Distance From Home', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 250])
fig.update_layout(title={'text': 'Distance From Home Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

bins = pd.IntervalIndex.from_tuples([(0, 5), (5, 10), (10, 20),(20, 30)])
Temp = Data[['Gender','Distance From Home','Attrition']]
Temp['Distance From Home'] = pd.cut(Temp['Distance From Home'], bins)
Temp = Temp.groupby(['Gender','Distance From Home','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Distance From Home'])
Temp['Distance From Home'] = Temp['Distance From Home'].astype(str)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Distance From Home', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Distance From Home', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 550)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 25], row=2, col=1)
fig.update_yaxes(title_text='Distance From Home', row=1, col=1)
fig.update_yaxes(title_text='Distance From Home', row=2, col=1)
fig.update_layout(title={'text': 'Distance From Home Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Education

Temp = Data[['Gender','Education','Attrition']]
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp.groupby(['Gender','Education','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Education'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Education', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Education', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 700)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Education', row=1, col=1)
fig.update_yaxes(title_text='Education', row=2, col=1)
fig.update_layout(title={'text': 'Education Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Education Field

Temp = Data.groupby(['Gender','Education Field','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Education Field'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Education Field', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Education Field', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 700)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 25], row=2, col=1)
fig.update_yaxes(title_text='Education Field', row=1, col=1)
fig.update_yaxes(title_text='Education Field', row=2, col=1)
fig.update_layout(title={'text': 'Education Field Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Temp = Data[['Education','Education Field','Attrition']]
Temp = Temp.groupby(['Education','Education Field','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Education Field'], inplace = True)
Temp['Education Field'] = Temp['Education Field'].map(lambda x: x.replace('&','and').replace(' ','\n'))

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Education Field', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,14])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Education Field', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Environment Satisfaction

Temp = Data[['Gender','Environment Satisfaction','Attrition']]
Temp['Environment Satisfaction'] = Temp['Environment Satisfaction'].replace(Categorical_Dict['Environment Satisfaction'])
Temp = Temp.groupby(['Gender','Environment Satisfaction','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Environment Satisfaction'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Environment Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Environment Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Environment Satisfaction', row=1, col=1)
fig.update_yaxes(title_text='Environment Satisfaction', row=2, col=1)
fig.update_layout(title={'text': 'Environment Satisfaction Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Hourly Rate

fig = px.histogram(Data, x = 'Hourly Rate', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 140])
fig.update_layout(title={'text': 'Hourly Rate Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

bins = pd.IntervalIndex.from_tuples([(25, 50), (50, 75), (75, 100)])
Temp = Data[['Gender','Hourly Rate','Attrition']]
Temp['Hourly Rate'] = pd.cut(Temp['Hourly Rate'], bins)
Temp = Temp.groupby(['Gender','Hourly Rate','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Hourly Rate'])
Temp['Hourly Rate'] = Temp['Hourly Rate'].astype(str)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Hourly Rate', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Hourly Rate', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 500)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Hourly Rate', row=1, col=1)
fig.update_yaxes(title_text='Hourly Rate', row=2, col=1)
fig.update_layout(title={'text': 'Hourly Rate Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

bins = pd.IntervalIndex.from_tuples([(25, 50), (50, 75), (75, 100)])
Temp = Data[['Education','Hourly Rate','Attrition']]
Temp['Hourly Rate'] = pd.cut(Temp['Hourly Rate'], bins)
Temp = Temp.groupby(['Education','Hourly Rate','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Hourly Rate'], inplace = True)
Temp['Hourly Rate'] = Temp['Hourly Rate'].astype(str)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Hourly Rate', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,14])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Hourly Rate', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Job Involvement

Temp = Data[['Gender','Job Involvement','Attrition']]
Temp['Job Involvement'] = Temp['Job Involvement'].replace(Categorical_Dict['Job Involvement'])
Temp = Temp.groupby(['Gender','Job Involvement','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Involvement'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Job Involvement', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Job Involvement', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Job Involvement', row=1, col=1)
fig.update_yaxes(title_text='Job Involvement', row=2, col=1)
fig.update_layout(title={'text': 'Job Involvement Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Job Level

Temp = Data[['Gender','Job Level','Attrition']]
Temp = Temp.groupby(['Gender','Job Level','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Level'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Job Level', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Job Level', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 700)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Job Level', row=1, col=1)
fig.update_yaxes(title_text='Job Level', row=2, col=1)
fig.update_layout(title={'text': 'Job Level Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Temp = Data[['Education','Job Level','Attrition']]
Temp = Temp.groupby(['Education','Job Level','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Level'])
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp = Temp[Temp.Percentage != 0]
Temp.sort_values(by=['Education Code','Job Level'], inplace = True)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Job Level', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,12])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Job Level', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Job Roles

Temp = Data[['Gender','Job Role','Attrition']]
Temp = Temp.groupby(['Gender','Job Role','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Role'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Job Role', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Job Role', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 1000)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 12], row=2, col=1)
fig.update_yaxes(title_text='Job Role', row=1, col=1)
fig.update_yaxes(title_text='Job Role', row=2, col=1)
fig.update_layout(title={'text': 'Job Role Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Job Satisfaction

Temp = Data[['Gender','Job Satisfaction','Attrition']]
Temp['Job Satisfaction'] = Temp['Job Satisfaction'].replace(Categorical_Dict['Job Satisfaction'])
Temp = Temp.groupby(['Gender','Job Satisfaction','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Satisfaction'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Job Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Job Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Job Satisfaction', row=1, col=1)
fig.update_yaxes(title_text='Job Satisfaction', row=2, col=1)
fig.update_layout(title={'text': 'Job Satisfaction Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Temp = Data[['Education','Job Satisfaction','Attrition']]
Temp = Temp.groupby(['Education','Job Satisfaction','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Job Satisfaction'])
Temp = Temp[Temp.Percentage != 0]
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp['Job Satisfaction'] = Temp['Job Satisfaction'].replace(Categorical_Dict['Job Satisfaction'])
Temp.sort_values(by=['Education Code','Job Satisfaction'], inplace = True)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Job Satisfaction', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,10])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Job Satisfaction', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Marital Status

Temp = Data[['Gender','Marital Status','Attrition']]
Temp = Temp.groupby(['Gender','Marital Status','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Marital Status'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Marital Status', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Marital Status', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 500)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 25], row=2, col=1)
fig.update_yaxes(title_text='Marital Status', row=1, col=1)
fig.update_yaxes(title_text='Marital Status', row=2, col=1)
fig.update_layout(title={'text': 'Marital Status Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Temp = Data[['Education','Marital Status','Attrition']]
Temp = Temp.groupby(['Education','Marital Status','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Marital Status'])
Temp = Temp[Temp.Percentage != 0]
Temp['Education Code'] = Temp['Education']
Temp['Education'] = Temp['Education'].replace(Categorical_Dict['Education'])
Temp.sort_values(by=['Education Code','Marital Status'], inplace = True)

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(13.5, 6),sharey = True)
_ = sns.barplot(ax= ax[0], x='Marital Status', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'No'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[0].set_title('Attrition: No', fontsize = 16)
_ = ax[0].set_ylim([0,20])
_ = ax[0].legend().set_visible(False)
_ = sns.barplot(ax= ax[1], x='Marital Status', y='Percentage', hue='Education', data=Temp[Temp.Attrition == 'Yes'],
                palette=sns.set_palette(Ed_Colors),
                edgecolor= Ed_LC)
_ = ax[1].set_title('Attrition: Yes', fontsize = 16)
_ = ax[1].legend(bbox_to_anchor=(1, 0.55), loc='center left',fontsize = 14)
_ = ax[1].set_ylabel(None)
plt.subplots_adjust(wspace=0.05)

for a in ax:
    _ = a.tick_params(labelsize=14)
#     _ = a.set_xticklabels(a.get_xticklabels(), rotation=45)
plt.subplots_adjust(wspace=0.05)

Number of Companies Worked

Temp = Data[['Gender','Number Of Companies Worked','Attrition']]
Temp = Temp.groupby(['Gender','Number Of Companies Worked','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Number Of Companies Worked'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Number Of Companies Worked', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Number Of Companies Worked', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 1000)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Number Of Companies Worked', row=1, col=1)
fig.update_yaxes(title_text='Number Of Companies Worked', row=2, col=1)
fig.update_layout(title={'text': 'Number Of Companies Worked Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Over Time

Temp = Data[['Gender','Over Time','Attrition']]
Temp = Temp.groupby(['Gender','Over Time','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Over Time'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Over Time', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Over Time', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 450)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='inside')
fig.update_xaxes(title_text='Percent', range=[0, 40], row=2, col=1)
fig.update_yaxes(title_text='Over Time', row=1, col=1)
fig.update_yaxes(title_text='Over Time', row=2, col=1)
fig.update_layout(title={'text': 'Over Time Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Percent Salary Hike

fig = px.histogram(Data, x = 'Percent Salary Hike', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 250])
fig.update_layout(title={'text': 'Percent Salary Hike Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

bins = pd.IntervalIndex.from_tuples([(10, 15), (15, 20), (20, 25)])
Temp = Data[['Gender','Percent Salary Hike','Attrition']]
Temp['Percent Salary Hike'] = pd.cut(Temp['Percent Salary Hike'], bins)
Temp = Temp.groupby(['Gender','Percent Salary Hike','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Percent Salary Hike'])
Temp['Percent Salary Hike'] = Temp['Percent Salary Hike'].astype(str)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Percent Salary Hike', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Percent Salary Hike', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 500)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 35], row=2, col=1)
fig.update_yaxes(title_text='Percent Salary Hike', row=1, col=1)
fig.update_yaxes(title_text='Percent Salary Hike', row=2, col=1)
fig.update_layout(title={'text': 'Percent Salary Hike Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Performance Rating

Temp = Data[['Gender','Performance Rating','Attrition']]
Temp['Performance Rating'] = Temp['Performance Rating'].replace(Categorical_Dict['Performance Rating'])
Temp = Temp.groupby(['Gender','Performance Rating','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Performance Rating'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Performance Rating', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Performance Rating', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 420)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 45], row=2, col=1)
fig.update_yaxes(title_text='Performance<br>Rating', row=1, col=1)
fig.update_yaxes(title_text='Performance<br>Rating', row=2, col=1)
fig.update_layout(title={'text': 'Performance Rating Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Relationship Satisfaction

Temp = Data[['Gender','Relationship Satisfaction','Attrition']]
Temp['Relationship Satisfaction'] = Temp['Relationship Satisfaction'].replace(Categorical_Dict['Relationship Satisfaction'])
Temp = Temp.groupby(['Gender','Relationship Satisfaction','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Relationship Satisfaction'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Relationship Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Relationship Satisfaction', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Relationship Satisfaction', row=1, col=1)
fig.update_yaxes(title_text='Relationship Satisfaction', row=2, col=1)
fig.update_layout(title={'text': 'Relationship Satisfaction Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Stock Option Level

Temp = Data[['Gender','Stock Option Level','Attrition']]
Temp = Temp.groupby(['Gender','Stock Option Level','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Stock Option Level'])

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Stock Option Level', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Stock Option Level', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 680)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 25], row=2, col=1)
fig.update_yaxes(title_text='Stock Option Level', row=1, col=1)
fig.update_yaxes(title_text='Stock Option Level', row=2, col=1)
fig.update_layout(title={'text': 'Stock Option Level Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Total Working Years

fig = px.histogram(Data, x = 'Total Working Years', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 250])
fig.update_layout(title={'text': 'Total Working Years Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

bins = pd.IntervalIndex.from_tuples([(-1, 10), (10, 20), (20, 30), (30, 40)])
Temp = Data[['Gender','Total Working Years','Attrition']]
Temp['Total Working Years'] = pd.cut(Temp['Total Working Years'], bins)
Temp = Temp.groupby(['Gender','Total Working Years','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
Temp = Temp.sort_values(by=['Total Working Years'])
Temp['Total Working Years'] = Temp['Total Working Years'].astype(str)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.08, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Total Working Years', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Total Working Years', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 35], row=2, col=1)
fig.update_yaxes(title_text='Total Working Years', row=1, col=1)
fig.update_yaxes(title_text='Total Working Years', row=2, col=1)
fig.update_layout(title={'text': 'Total Working Years Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Training Times Last Year

fig = px.histogram(Data, x = 'Total Working Years', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 250])
fig.update_layout(title={'text': 'Total Working Years Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

Work-Life Balance Score

Temp = Data[['Gender','Work Life Balance','Attrition']]
Temp['Work Life Balance'] = Temp['Work Life Balance'].replace(Categorical_Dict['Work Life Balance'])
Temp = Temp.groupby(['Gender','Work Life Balance','Attrition'])['Attrition'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)

# Figures

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Attrition: No', 'Attrition: Yes'))
# Top
fig1 = px.bar(Temp.loc[Temp.Attrition == 'No'], y= 'Work Life Balance', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Attrition == 'Yes'], y= 'Work Life Balance', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = MF_Colors)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= MF_LC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Work Life Balance', row=1, col=1)
fig.update_yaxes(title_text='Work Life Balance', row=2, col=1)
fig.update_layout(title={'text': 'Work Life Balance Distribution by Gender and Attrition',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Years at the Company

fig = px.histogram(Data, x = 'Years At Company', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 250])
fig.update_layout(title={'text': 'Years At Company Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

Years In Current Role

fig = px.histogram(Data, x = 'Years In Current Role', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 400])
fig.update_layout(title={'text': 'Years In Current Role Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

Years Since Last Promotion

fig = px.histogram(Data, x = 'Years Since Last Promotion', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 600])
fig.update_layout(title={'text': 'Years Since Last Promotion Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

Years With Current Manager

fig = px.histogram(Data, x = 'Years With Current Manager', color='Attrition', marginal= 'box', color_discrete_sequence= Att_Colors,
                  hover_data=Data.columns)
fig.update_layout(title = 'Credit Distribution Histogram', plot_bgcolor= 'white')
fig.update_traces(marker_line_color= Att_LC, marker_line_width=0.5, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig['layout']['yaxis'].update(range=[0, 350])
fig.update_layout(title={'text': 'Years With Current Manager Distribution by Attrition',
                         'x':0.46, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'},
                 yaxis_title='Frequency')
fig.show()

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